UX Portfolio
This work sample demonstrates my ability to understand human experiences through mixed behavioral research methods and translate complex data into actionable insights.
About Me
I am a psychology-trained researcher with experience in longitudinal behavioral research, survey-based studies, statistical modeling, and research communication.
My work focuses on understanding human needs, emotional experiences, and behavioral patterns through structured research methods.
Although these projects were conducted in academic contexts, they demonstrate transferable UX research skills, including diary study design, behavioral data analysis, user segmentation, insight synthesis, and evidence-based communication.
Case 1
A 7-day diary study on subjective well-being, hope, academic stress, and daily need satisfaction.
Project Context
High school students' well-being is not static. Their emotional experiences may fluctuate from day to day depending on academic stress, hope, and psychological need satisfaction. This project aimed to understand these daily dynamics using a longitudinal diary design.
Research Questions
Contribution and Analysis Process
Screened and cleaned repeated-measures data to ensure accuracy, completeness, and consistency.
Organized diary data into an analysis-ready format suitable for daily-level modeling.
Conducted multilevel modeling in R to distinguish within-person fluctuations from between-person differences.
Contributed to result interpretation, visualization, and manuscript preparation.
Selected Figures
Visual summaries of the study model, three-way interaction patterns, and gender moderation effects.
Key Insights
Students may experience meaningful day-to-day changes in emotional states, which suggests the importance of tracking experiences over time rather than relying only on one-time measurements.
Daily hope can help explain why students feel better on some days than others, highlighting the value of psychological resources in stressful academic contexts.
Some students may generally experience higher stress than others, while the same student may also experience unusually stressful days. These two patterns require different interpretations.
Case 2
A person-centered research case using latent profile analysis.
Project Context
Burnout is not experienced in the same way by all teachers. Instead of assuming a single average pattern, this project used a person-centered approach to identify distinct burnout profiles among female kindergarten teachers.
Research Questions
From Latent Profiles to Human Segments
High organizational climate resources, but comparatively lower social well-being and trait hope.
Human segment interpretationThese teachers may work in a relatively supportive organizational environment, but their internal psychological and social resources are not equally strong.
High levels across organizational climate, social well-being, and trait hope.
Human segment interpretationThese teachers appear resource-rich across both workplace and personal domains. However, the manuscript shows that this group reported the highest levels of burnout across emotional exhaustion, depersonalization, and reduced personal accomplishment.
Lower organizational climate, social well-being, and trait hope.
Human segment interpretationThis group showed limited psychosocial resources across both external and internal domains. Interestingly, the manuscript reports that this group had relatively lower burnout levels than expected.
Lower organizational climate resources, but higher social well-being and trait hope.
Human segment interpretationThese teachers may rely more on internal and social psychological resources, even when the organizational environment is less supportive.
The four profiles show that psychosocial resources do not translate into burnout outcomes in a simple linear way. A person-centered approach reveals distinct teacher segments with different resource configurations, burnout risks, and support needs.
Key Insights
Teachers with similar overall burnout scores may still show different patterns across emotional exhaustion, depersonalization, and personal accomplishment.
Different burnout profiles may require different types of support rather than one-size-fits-all interventions.
By identifying distinct profiles, research findings can be translated into more precise support recommendations for schools, organizations, or service systems.
In UX research, similar segmentation logic can be used to identify different user types, prioritize user needs, and develop persona-informed product or service strategies.
What These Cases Demonstrate
Together, these projects show how my psychology research background can support UX research work that requires curiosity, empathy, analytical rigor, and clear communication.
I can translate human experience questions into structured research designs.
I can analyze how experiences change over time rather than relying only on one-time measurements.
I can clean, structure, analyze, and visualize complex behavioral datasets.
I can identify meaningful subgroups and translate statistical patterns into human-centered insights.
I can turn complex research findings into clear, actionable narratives for non-specialist audiences.
From Academic Research to UX Research
| Academic Research Practice | Core Research Capability | UX Research Application |
|---|---|---|
| Diary Study | Tracking experience over time | Diary studies, onboarding research, retention experience tracking |
| Daily emotional fluctuation | Understanding changing user states | Identifying how users' needs, emotions, and pain points evolve across contexts |
| Within-person analysis | Context-sensitive behavior analysis | Understanding how the same user behaves differently under different situations |
| Between-person differences | Individual difference thinking | User segmentation and identifying different experience patterns |
| Multilevel modeling | Analyzing nested behavioral data | Interpreting repeated user data, session-level data, or longitudinal feedback |
| Latent profile analysis | Pattern recognition and segmentation | User segmentation, behavioral clustering, persona development |
| Burnout profiles | Translating data into human groups | Creating evidence-informed user types or need-based personas |
| Model comparison | Evidence-based decision-making | Validating segment structures or prioritizing research interpretations |
| Post-profile comparison | Comparing needs across groups | Identifying different pain points, motivations, and support needs across user segments |